We are gratefully funded by the Mercator Research Center Ruhr (Mercur)

Research

Machine Learning is concerned with algorithmic and statistical aspects
of automated training of adaptive predictive models from data.
Learning systems have entered industrial applications as well as the
analysis of scientific data on a grand scale.

Support Vector Machines (SVMs) and kernel-based methods in general are
well established for their accurate predictions, their statistical
properties, and their high flexibility. One of their main disadvantages
is high training complexity, which renders exact modeling of large data
bases impractical. Resorting to simpler (e.g., linear) models for
algorithmic reasons is to be avoided from a statistical point of view.
In contrast, large data sets are an ideal prerequisite for training
high-quality non-linear models without restrictive assumptions.
Numerous approximate training schemes have been proposed for making
non-linear SVMs applicable to large amounts of data. These approaches,
in contrast to exact SVM models, are neither fully developed nor well
understood from a learning and complexity theoretical point of view.

The project aims at closing this gap. The project partners will
investigate the relationship of training time and statistical guarantees,
relying on empirical as well as formal methods. We are conducting a
comparison of different approximative SVM solvers. This will shed light
on the trade-offs of the different methods. In addition new Methods
will be developed and analyzed. The resulting insights and algorithms
will be applied to relevant large-scale learning problems in the areas
of astrophysics, medical imaging, driver assistance systems, and sports
analytics.

Publications

Downloads

All of our experiments are conducted in R using several toolboxes,
many of them were developed at our institute.

mlr
mlr is an R package that allows to conduct machine learning problems

mlrMBO
mlrMBO is an R package that builds upon the power of mlr and provides easy model-based optimization.

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